Multiple lines of evidence point to a distinctly non-random organization of functional elements in the genome. It appears that the mouse genome is organized into domains-contiguous, possibly overlapping, segments of chromosomes containing functionally related groups of elements (including, but not limited to genes and regulatory elements) and that non-contiguous domains and isolated elements interact in networks. This project will develop the bioinformatics tools required for using multiple, diverse data sets in analyzing these relationships. Aim 2a. we will develop methods and software to identify and characterize domains, moving from analyses based on dynamic programming to more sophisticated methods based on Hidden Markov Models. We will include methods for combining multiple data sources to form composite domain structures. Aim 2b. we will generate the networks implied by disparate data, using a graph representation in which nodes are chromosome positions (or intervals) and edges imply an interaction (direct or indirect) between the nodes. As with domains, we will generate both data-specific and composite networks for analysis. We will develop methods and software to compare alternative networks, focusing specifically on methods to identify statistically significant common subnets. Aim 2c. we will provide our computational tools to the other projects in this proposal for the analysis of data generated by this grant (e.g., gene expression data from Project by Churchill, and recombination data from Project by Petkov, as well as overall integration in Project by Paigen), as well as data obtained from external sources (e.g., GO annotations or the KEGG database). We will work also with the Computational Core to define efficient and comprehensive databases and web interfaces for the data and analysis we produce.